All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 25579 14719 966
PDF Downloads 13725 6677 239

The Global Land Data Assimilation System

M. Rodell
Search for other papers by M. Rodell in
Current site
Google Scholar
PubMed
Close
,
P. R. Houser
Search for other papers by P. R. Houser in
Current site
Google Scholar
PubMed
Close
,
U. Jambor
Search for other papers by U. Jambor in
Current site
Google Scholar
PubMed
Close
,
J. Gottschalck
Search for other papers by J. Gottschalck in
Current site
Google Scholar
PubMed
Close
,
K. Mitchell
Search for other papers by K. Mitchell in
Current site
Google Scholar
PubMed
Close
,
C.-J. Meng
Search for other papers by C.-J. Meng in
Current site
Google Scholar
PubMed
Close
,
K. Arsenault
Search for other papers by K. Arsenault in
Current site
Google Scholar
PubMed
Close
,
B. Cosgrove
Search for other papers by B. Cosgrove in
Current site
Google Scholar
PubMed
Close
,
J. Radakovich
Search for other papers by J. Radakovich in
Current site
Google Scholar
PubMed
Close
,
M. Bosilovich
Search for other papers by M. Bosilovich in
Current site
Google Scholar
PubMed
Close
,
J. K. Entin
Search for other papers by J. K. Entin in
Current site
Google Scholar
PubMed
Close
,
J. P. Walker
Search for other papers by J. P. Walker in
Current site
Google Scholar
PubMed
Close
,
D. Lohmann
Search for other papers by D. Lohmann in
Current site
Google Scholar
PubMed
Close
, and
D. Toll
Search for other papers by D. Toll in
Current site
Google Scholar
PubMed
Close
Full access

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The ongoing GLDAS archive (started in 2001) of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.

Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

Hydrological Sciences Branch, and Data Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Goddard Earth Science and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland

Goddard Earth Science and Technology Center, University of Maryland, Baltimore County, Baltimore, and NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

Department of Civil and Environmental Engineering, University of Melbourne, Melbourne, Victoria, Australia

*Current affiliation: NASA, Washington, D.C.

CORRESPONDING AUTHOR: Dr. Matthew Rodell, Hydrological Sciences Branch, NASA Goddard Space Flight Center, Code 974.1, Greenbelt, MD 20771, E-mail: Matthew.Rodell@nasa.gov

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The ongoing GLDAS archive (started in 2001) of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.

Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

Hydrological Sciences Branch, and Data Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Goddard Earth Science and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland

Goddard Earth Science and Technology Center, University of Maryland, Baltimore County, Baltimore, and NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

Department of Civil and Environmental Engineering, University of Melbourne, Melbourne, Victoria, Australia

*Current affiliation: NASA, Washington, D.C.

CORRESPONDING AUTHOR: Dr. Matthew Rodell, Hydrological Sciences Branch, NASA Goddard Space Flight Center, Code 974.1, Greenbelt, MD 20771, E-mail: Matthew.Rodell@nasa.gov
Save